Microsoft Dp-600 Fabric analytics Practice Exam
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Official Name: Microsoft DP-600 Fabric Analytics Engineer Associate
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Start NowAbout the Microsoft Dp-600 Fabric analytics Certification
The Microsoft Dp-600 Fabric analytics certification is designed for professionals who design and deploy enterprise-scale analytics solutions using Microsoft Fabric. they build semantic models, data warehouses, and lakehouses while securing and optimizing assets with SQL, DAX, and KQL. As technology evolves and industry demands grow more complex; this credential validates your ability to apply real-world skills and knowledge using Microsoft tools and frameworks. Earning the certification positions you as a trusted expert, capable of solving high-impact challenges and contributing to secure, scalable, and efficient systems.
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Test Your Knowledge of Microsoft Dp-600 Fabric analytics
Question #1
A company wants to implement a medallion architecture in Microsoft Fabric for its sales data.
Which Fabric component should be used to store raw data in the bronze layer?
A) Lakehouse
B) Warehouse
C) Notebook
D) Semantic model
Solution
Correct answers: A – Explanation:
Lakehouse is ideal for storing raw, unprocessed data in the bronze layer of a medallion architecture.
Question #2
A data engineer needs to create a semantic model for business users to explore sales metrics.
Which Fabric feature supports this requirement?
A) Power BI semantic model
B) Lakehouse
C) Notebook
D) Pipeline
Solution
Correct answers: A – Explanation:
Power BI semantic models allow business users to interact with curated data using visuals and natural language queries.
Question #3
A team wants to ingest data from an external SQL database into Fabric for analytics.
Which tool should they use?
A) Data pipeline
B) Notebook
C) Semantic model
D) Lakehouse
Solution
Correct answers: A – Explanation:
Data pipelines in Fabric support ingestion from external sources like SQL databases into lakehouses or warehouses.
Question #4
A company needs to schedule daily refreshes of its warehouse tables.
Which Fabric feature supports this automation?
A) Pipeline scheduling
B) Notebook triggers
C) Semantic model refresh
D) Lakehouse sync
Solution
Correct answers: A – Explanation:
Pipeline scheduling enables automated data refreshes and transformations at defined intervals.
Question #5
A data engineer wants to transform data using PySpark before loading it into a warehouse.
Which Fabric component should be used?
A) Notebook
B) Semantic model
C) Dataflow
D) Pipeline
Solution
Correct answers: A – Explanation:
Notebooks in Fabric support PySpark for advanced data transformation and processing tasks.
Question #6
An organization wants to implement version control for its Fabric analytics assets.
Which integration supports this requirement?
A) Git integration
B) Pipeline history
C) Notebook snapshots
D) Semantic model export
Solution
Correct answers: A – Explanation:
Git integration in Fabric allows version control of notebooks, pipelines, and other analytics assets.
Question #7
A team needs to monitor data pipeline failures and receive alerts.
Which Fabric feature supports this?
A) Pipeline monitoring and alerting
B) Notebook logs
C) Semantic model diagnostics
D) Semantic model
Solution
Correct answers: A – Explanation:
Pipeline monitoring and alerting enables visibility into failures and supports proactive issue resolution.
Question #8
A company wants to share a semantic model across multiple workspaces.
Which feature should be used?
A) Cross-workspace dataset sharing
B) Notebook linking
C) Lakehouse replication
D) Pipeline export
Solution
Correct answers: A – Explanation:
Lakehouse is ideal for storing raw, unprocessed data in the bronze layer of a medallion architecture.
Question #9
A data engineer needs to optimize query performance in a Fabric warehouse.
Which strategy should be applied?
A) Use distribution and indexing options
B) Notebook caching
C) Lakehouse partitioning
D) Semantic model filtering
Solution
Correct answers: A – Explanation:
Distribution and indexing in warehouses improve query performance by organizing data efficiently.
Question #10
A business wants to secure access to Fabric assets based on user roles.
Which feature supports this requirement?
A) Workspace role assignments
B) Notebook permissions
C) Pipeline encryption
D) Lakehouse masking
Solution
Correct answers: A – Explanation:
Workspace role assignments control access to Fabric assets based on predefined user roles and responsibilities.
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